scholarly journals Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices

2019 ◽  
Vol 646 ◽  
pp. 012048 ◽  
Author(s):  
Tiancheng Xia ◽  
Richard Jiang ◽  
Yong Qing Fu ◽  
Nanlin Jin
Keyword(s):  
Deep Learning ◽  
Blood Cell ◽  
Medical Devices ◽  
Point Of Care ◽  
Cell Detection ◽  
Detection And Counting

scholarly journals AI-enabled microscopic blood analysis for microfluidic COVID-19 haematology

2020 ◽  
Author(s):  
Tiancheng Xia ◽  
Richard Fu ◽  
Nanlin Jin ◽  
Paul Chazot ◽  
Plamen Angelov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Cell ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Point Of Care ◽  
White Blood Cells ◽  
Blood Analysis ◽  
Cell Analysis ◽  
Learning Methods ◽  
Cell Counts

Microscopic blood cell analysis is an important methodology for medical diagnosis, and complete blood cell counts (CBCs) are one of the routine tests operated in hospitals. Results of the CBCs include amounts of red blood cells, white blood cells and platelets in a unit blood sample. It is possible to diagnose diseases such as anemia when the numbers or shapes of red blood cells become abnormal. The percentage of white blood cells is one of the important indicators of many severe illnesses such as infection and cancer. The amounts of platelets are decreased when the patient suffers hemophilia. Doctors often use these as criteria to monitor the general health conditions and recovery stages of the patients in the hospital. However, many hospitals are relying on expensive hematology analyzers to perform these tests, and these procedures are often time consuming. There is a huge demand for an automated, fast and easily used CBCs method in order to avoid redundant procedures and minimize patients’ burden on costs of healthcare. In this research, we investigate a new CBC detection method by using deep neural networks, and discuss state of the art machine learning methods in order to meet the medical usage requirements. The approach we applied in this work is based on YOLOv3 algorithm, and our experimental results show the applied deep learning algorithms have a great potential for CBCs tests, promising for deployment of deep learning methods into microfluidic point-of-care medical devices. As a case of study, we applied our blood cell detector to the blood samples of COVID-19 patients, where blood cell clots are a typical symptom of COVID-19.

scholarly journals AI-enabled microscopic blood analysis for microfluidic COVID-19 haematology

2020 ◽  
Author(s):  
Tiancheng Xia ◽  
Richard Fu ◽  
Nanlin Jin ◽  
Paul Chazot ◽  
Plamen Angelov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Cell ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Point Of Care ◽  
White Blood Cells ◽  
Blood Analysis ◽  
Cell Analysis ◽  
Learning Methods ◽  
Cell Counts

Microscopic blood cell analysis is an important methodology for medical diagnosis, and complete blood cell counts (CBCs) are one of the routine tests operated in hospitals. Results of the CBCs include amounts of red blood cells, white blood cells and platelets in a unit blood sample. It is possible to diagnose diseases such as anemia when the numbers or shapes of red blood cells become abnormal. The percentage of white blood cells is one of the important indicators of many severe illnesses such as infection and cancer. The amounts of platelets are decreased when the patient suffers hemophilia. Doctors often use these as criteria to monitor the general health conditions and recovery stages of the patients in the hospital. However, many hospitals are relying on expensive hematology analyzers to perform these tests, and these procedures are often time consuming. There is a huge demand for an automated, fast and easily used CBCs method in order to avoid redundant procedures and minimize patients’ burden on costs of healthcare. In this research, we investigate a new CBC detection method by using deep neural networks, and discuss state of the art machine learning methods in order to meet the medical usage requirements. The approach we applied in this work is based on YOLOv3 algorithm, and our experimental results show the applied deep learning algorithms have a great potential for CBCs tests, promising for deployment of deep learning methods into microfluidic point-of-care medical devices. As a case of study, we applied our blood cell detector to the blood samples of COVID-19 patients, where blood cell clots are a typical symptom of COVID-19.

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

scholarly journals Optical-Based Biosensors and Their Portable Healthcare Devices for Detecting and Monitoring Biomarkers in Body Fluids

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1285
Author(s):  
Anh Tran Tam Pham ◽  
Angus Wallace ◽  
Xinyi Zhang ◽  
Damian Tohl ◽  
Hao Fu ◽  
...  
Keyword(s):  
Medical Devices ◽  
Point Of Care ◽  
Body Fluids ◽  
Optical Measurements ◽  
Mobile Technologies ◽  
Detection Methods ◽  
Portable Devices ◽  
Bodily Fluids ◽  
Clinical Measurements ◽  
Distinct Features

The detection and monitoring of biomarkers in body fluids has been used to improve human healthcare activities for decades. In recent years, researchers have focused their attention on applying the point-of-care (POC) strategies into biomarker detection. The evolution of mobile technologies has allowed researchers to develop numerous portable medical devices that aim to deliver comparable results to clinical measurements. Among these, optical-based detection methods have been considered as one of the common and efficient ways to detect and monitor the presence of biomarkers in bodily fluids, and emerging aggregation-induced emission luminogens (AIEgens) with their distinct features are merging with portable medical devices. In this review, the detection methodologies that use optical measurements in the POC systems for the detection and monitoring of biomarkers in bodily fluids are compared, including colorimetry, fluorescence and chemiluminescence measurements. The current portable technologies, with or without the use of smartphones in device development, that are combined with optical biosensors for the detection and monitoring of biomarkers in body fluids, are also investigated. The review also discusses novel AIEgens used in the portable systems for the detection and monitoring of biomarkers in body fluid. Finally, the potential of future developments and the use of optical detection-based portable devices in healthcare activities are explored.

scholarly journals Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

2021 ◽  
Vol 13 (14) ◽  
pp. 2822
Author(s):  
Zhe Lin ◽  
Wenxuan Guo
Keyword(s):  
Deep Learning ◽  
Cotton Plant ◽  
Unmanned Aerial System ◽  
Learning Models ◽  
Training Images ◽  
Testing Dataset ◽  
Cotton Plants ◽  
Detection And Counting ◽  
Different Dimensions ◽  
The Mean

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R2), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.

scholarly journals Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2629
Author(s):  
Kunkyu Lee ◽  
Min Kim ◽  
Changhyun Lim ◽  
Tai-Kyong Song
Keyword(s):  
Deep Learning ◽  
Ultrasound Imaging ◽  
Mobile Device ◽  
Network Architecture ◽  
Point Of Care ◽  
Ultrasound Image ◽  
Training Dataset ◽  
Point Of Care Ultrasound ◽  
Reverse Scan ◽  
Scan Conversion

Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.

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